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Graph-based segmentation with homogeneous hue and texture vertices

Ngo Lua, Han Jae-Ho

Optica Applicata

2021

Vol. 51, nr 4

541--549

This work presents an automated segmentation method, based on graph theory, which processes superpixels that exhibit spatially similarities in hue and texture pixel groups, rather than individual pixels. The graph shortest path includes a chain of neighboring superpixels which have minimal intensity changes. This method reduces graphics computational complexity because it provides large decreases in the number of vertices as the superpixel size increases. For the starting vertex prediction, the boundary pixel in first column which is included in this starting vertex is predicted by a trained deep neural network formulated as a regression task. By formulating the problem as a regression scheme, the computational burden is decreased in comparison with classifying each pixel in the entire image. This feasibility approach, when applied as a preliminary study in electron microscopy and optical coherence tomography images, demonstrated high measures of accuracy: 0.9670 for the electron microscopy image and 0.9930 for vitreous/nerve-fiber and inner-segment/outer-segment layer segmentations in the optical coherence tomography image.

Intraoperative imaging based on common-path time-domain reflectometry for brain tumor surgery

Han Jae-Ho, Cha Jaepyeong

Optica Applicata

2020

Vol. 50, nr 2

223--227

Minimally invasive intraoperative imaging plays a crucial role in delicate microsurgeries for precise operation monitoring in which fiber optic imaging can be considered as an endoscopy and surgical proximity guidance tool due to its compactness. This paper presents a near-infrared time-domain reflectometric common-path optical coherence tomography imaging technique using a bare-fiber probe mounted directly on a scanning galvanometer. The common-path setup allows the use of a freely adjustable optical path length and a disposable fiber probe, as well as eliminating the need for an additional dedicated reference optical path. Experimental results demonstrate clear discrimination between the brain tumor tissue and the normal tissue for mouse brains with the images acquired in real-time over a wide area. The proposed method enables real-time and in situ visualization of tumor resection for intraoperative imaging, and this study demonstrates the feasibility of its application to microsurgical interventions.